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SentEmoji: A Dataset to Generate Empathising Conversations

Published: 15 January 2020 Publication History

Abstract

Emojis are gaining popularity in day-to-day computer-mediated conversations, resulting in more interactive conversations. On the other hand, traditional chatbots lack the ability to use emojis effectively for creating an engaging and empathising conversation even after recognising feelings of the conversation partner, an essential communicative skill. This inability is majorly due to the paucity of any such suitable publicly available datasets and framework for training and evaluation of chatbot. Prior work has either classified the emojis or generated empathy dialogue without the use of emojis. Through this work, we propose a new dataset SentEmoji, generated using public dataset EmpathyDialogues, and its mapping to relevant emojis using EmojiNet dataset. We present a novel approach to generate dialogue with emojis to express empathy. A study will be conducted to get user rating on three aspects - empathy/sympathy, relevance and fluency. The comparison of this user-study with prior studies will reflect the effectiveness of this approach.

References

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Jamie Fraser, Ioannis Papaioannou, and Oliver Lemon. 2018. Spoken conversational AI in video games -- Emotional dialogue management increases user engagement. In Proceedings of the 18th International Conference on Intelligent Virtual Agents. ACM, 179--184. https://doi.org/10.1145/3267851.3267896
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Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.
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Ji Ho Park, Peng Xu, and Pascale Fung. 2018. PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags. CoRR abs/1804.08280 (2018). arXiv:1804.08280 http://arxiv.org/abs/1804.08280
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Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2018. I Know the Feeling: Learning to Converse with Empathy. CoRR abs/1811.00207 (2018). arXiv:1811.00207 http://arxiv.org/abs/1811.00207
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Sanjaya Wijeratne, Lakshika Balasuriya, Amit P. Sheth, and Derek Doran. 2016. EmojiNet: Building a Machine Readable Sense Inventory for Emoji. CoRR abs/1610.07710 (2016). arXiv:1610.07710 http://arxiv.org/abs/1610.07710

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cover image ACM Other conferences
CoDS COMAD 2020: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD
January 2020
399 pages
ISBN:9781450377386
DOI:10.1145/3371158
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 15 January 2020

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  • Short-paper
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CoDS COMAD 2020
CoDS COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD
January 5 - 7, 2020
Hyderabad, India

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CoDS COMAD 2020 Paper Acceptance Rate 78 of 275 submissions, 28%;
Overall Acceptance Rate 197 of 680 submissions, 29%

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